Grammarly Machine Learning Engineer Interview Questions + Guide in 2025

Grammarly is a leading AI writing assistance company that supports over 30 million users and 70,000 professional teams, including those at 96% of the Fortune 500. Its mission is to enhance communication through innovative technology, making it a trusted partner in effective writing.

As a Machine Learning Engineer at Grammarly, you will play a pivotal role in the On-Device team, focusing on leveraging cutting-edge machine learning techniques to enhance user experience. This position involves proposing, designing, and prototyping new features that capitalize on on-device capabilities, building and implementing machine learning models, and collaborating with cross-functional teams to integrate these models into user interfaces. Key responsibilities include launching and monitoring model deployments through A/B testing, responding to user feedback, and ensuring that the technology remains at the forefront of advancements in AI and NLP.

Ideal candidates will possess a deep understanding of modern algorithms and approaches to machine learning and natural language processing, along with proficiency in a modern compiled language, such as Rust. Experience with cross-platform projects (macOS, Windows, Linux, and Android) and a strong ability to communicate complex technical concepts effectively are essential. Additionally, embodying Grammarly's EAGER values—ethical, adaptable, gritty, empathetic, and remarkable—will be critical to thriving in this fast-paced and innovative environment.

This guide will help you prepare for your interview by providing insights into the skills and attributes Grammarly values in Machine Learning Engineers, allowing you to tailor your responses and showcase your fit for the role effectively.

The interview process for a Machine Learning Engineer at Grammarly is structured and thorough, reflecting the company's commitment to finding the right fit for their innovative team. Here’s what you can expect:

1. Initial Recruiter Call

The process typically begins with a phone call from a recruiter. This initial conversation lasts about 30 minutes and serves as an opportunity for the recruiter to discuss the role, the company culture, and your background. They will assess your interest in the position and gauge your fit for Grammarly's values and work environment.

2. Technical Screening

Following the recruiter call, candidates usually undergo a technical screening. This may involve a coding challenge or a take-home assignment that tests your machine learning knowledge and coding skills. The focus is often on practical applications relevant to the role, such as implementing algorithms or solving real-world problems.

3. Hiring Manager Interview

After successfully passing the technical screening, candidates typically have a one-on-one interview with the hiring manager. This discussion dives deeper into your technical expertise, past experiences, and how you approach problem-solving. The hiring manager will also assess your alignment with the team’s goals and the company’s mission.

4. Virtual Onsite Interviews

Candidates who progress past the hiring manager interview are invited to participate in a virtual onsite interview. This stage usually consists of multiple rounds, each lasting around 45-75 minutes. The interviews cover a range of topics, including: - Technical Questions: Expect to answer questions related to machine learning algorithms, system design, and coding challenges that may involve data structures and algorithms. - Behavioral Questions: These questions aim to understand your work style, how you handle challenges, and your ability to collaborate with others. Be prepared to discuss past experiences and how they relate to the role. - Case Studies: You may be presented with real-world scenarios relevant to Grammarly's products and asked to propose solutions or improvements.

5. Final Interviews

In some cases, there may be additional interviews with senior team members or cross-functional partners. These discussions often focus on your ability to work collaboratively across teams and your understanding of how machine learning can enhance Grammarly's offerings.

6. Reference Checks

If you successfully navigate the interview rounds, the final step typically involves reference checks. Grammarly may reach out to previous employers or colleagues to verify your experience and skills.

As you prepare for your interview, keep in mind that the questions will likely focus on both your technical abilities and your fit within Grammarly's unique culture. Now, let’s explore the types of interview questions you might encounter during this process.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Grammarly's interview process is well-structured and thorough, often involving multiple rounds that assess both technical and behavioral competencies. Familiarize yourself with the typical stages: a recruiter call, a technical screen, and a virtual onsite that includes coding interviews, system design discussions, and behavioral interviews. Knowing what to expect will help you prepare effectively and reduce anxiety.

Emphasize Collaboration and Communication

Grammarly values teamwork and effective communication. During your interviews, highlight your experiences working in cross-functional teams and your ability to communicate complex technical concepts to non-technical stakeholders. Be prepared to discuss how you’ve collaborated with product teams or other engineers to deliver successful projects.

Showcase Your Technical Skills

As a Machine Learning Engineer, you will be expected to demonstrate a deep understanding of modern algorithms, particularly in ML and NLP. Brush up on your coding skills in languages like Rust, Python, and C++. Be ready to solve problems on the spot and explain your thought process clearly. Practice coding challenges that focus on data structures, algorithms, and system design, as these are common topics in technical interviews.

Prepare for Behavioral Questions

Grammarly places a strong emphasis on cultural fit and values alignment. Be prepared to answer behavioral questions that reflect their EAGER values (ethical, adaptable, gritty, empathetic, and remarkable). Use the STAR method (Situation, Task, Action, Result) to structure your responses, focusing on how your past experiences align with Grammarly's mission and values.

Leverage Your Research Skills

Given the emphasis on leveraging academic research, be prepared to discuss how you stay updated with the latest advancements in ML and NLP. You might be asked to reference recent papers or techniques that could be applicable to Grammarly's products. Demonstrating your ability to integrate cutting-edge research into practical applications will set you apart.

Be Ready for A/B Testing Discussions

As part of your role, you will likely be involved in launching and monitoring ML models through A/B testing. Familiarize yourself with the principles of A/B testing, including how to design experiments, analyze results, and iterate based on user feedback. Be prepared to discuss any relevant experiences you have in this area.

Show Enthusiasm for the Company Culture

Grammarly is known for its positive and inclusive culture. During your interviews, express your enthusiasm for their mission and values. Share why you want to work at Grammarly specifically, and how you can contribute to their goals. This will demonstrate your genuine interest in the company and help you connect with your interviewers.

Follow Up Thoughtfully

After your interviews, send a thoughtful thank-you note to your interviewers. Mention specific topics discussed during the interview that resonated with you, and reiterate your excitement about the opportunity to join Grammarly. This not only shows your professionalism but also reinforces your interest in the role.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Grammarly. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Grammarly. The interview process will assess your technical skills in machine learning, natural language processing, coding proficiency, and your ability to collaborate effectively within a team. Be prepared to discuss your past experiences, problem-solving approaches, and how you align with Grammarly's values.

Machine Learning and NLP

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the characteristics of both learning types, emphasizing how supervised learning uses labeled data while unsupervised learning deals with unlabeled data.

Example

“Supervised learning involves training a model on a labeled dataset, where the input-output pairs are known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning finds patterns in data without predefined labels, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them.

Example

“I worked on a sentiment analysis project where we aimed to classify customer reviews. One challenge was dealing with imbalanced data. I implemented techniques like oversampling the minority class and using different evaluation metrics to ensure our model was robust.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, while precision and recall are crucial for imbalanced datasets. For instance, in a fraud detection model, I prioritize recall to minimize false negatives.”

4. What is overfitting, and how can it be prevented?

This question gauges your understanding of model generalization.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent it, I use techniques like cross-validation to ensure the model generalizes well and apply regularization methods to penalize overly complex models.”

5. Can you explain the concept of transfer learning?

This question assesses your knowledge of advanced machine learning techniques.

How to Answer

Define transfer learning and provide an example of its application.

Example

“Transfer learning involves taking a pre-trained model on a large dataset and fine-tuning it on a smaller, task-specific dataset. For instance, using a model trained on ImageNet for a specific image classification task can significantly reduce training time and improve performance.”

Coding and Algorithms

1. Write a function to implement a basic linear regression model.

This question tests your coding skills and understanding of algorithms.

How to Answer

Discuss the steps involved in implementing linear regression, including data preparation, model training, and prediction.

Example

“I would start by importing necessary libraries, then define a function that takes in training data, fits a linear model using gradient descent, and returns the coefficients. Finally, I would implement a predict function to make predictions on new data.”

2. How would you optimize a slow-running algorithm?

This question evaluates your problem-solving and optimization skills.

How to Answer

Discuss strategies such as algorithmic improvements, data structure changes, and parallel processing.

Example

“To optimize a slow algorithm, I would first analyze its time complexity and identify bottlenecks. For instance, if a nested loop is causing delays, I might explore using a hash table to reduce lookup times or implement memoization to cache results of expensive function calls.”

3. Explain the time and space complexity of your favorite sorting algorithm.

This question tests your understanding of algorithm efficiency.

How to Answer

Choose a sorting algorithm, explain its mechanics, and discuss its complexity.

Example

“I prefer quicksort due to its efficiency. The average time complexity is O(n log n), while the worst-case is O(n^2). It uses O(log n) space for the recursive stack, making it suitable for large datasets.”

4. How would you handle missing data in a dataset?

This question assesses your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, removal, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of missingness. Depending on the situation, I might impute missing values using the mean or median for numerical data or the mode for categorical data. If the missing data is substantial, I may consider removing those records or using algorithms that can handle missing values directly.”

5. Can you describe a time when you had to debug a complex issue in your code?

This question evaluates your debugging skills and persistence.

How to Answer

Share a specific instance, the steps you took to identify the issue, and how you resolved it.

Example

“I encountered a bug in a machine learning pipeline where the model predictions were consistently off. I systematically checked each step, from data preprocessing to model training. I discovered that a feature was being incorrectly scaled, which I fixed, leading to a significant improvement in model accuracy.”

Behavioral and Cultural Fit

1. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, such as using frameworks or tools to manage tasks.

Example

“I prioritize tasks based on deadlines and impact. I use tools like Trello to visualize my workload and apply the Eisenhower Matrix to distinguish between urgent and important tasks, ensuring I focus on what drives the most value.”

2. Describe a situation where you had to work with a difficult team member.

This question evaluates your interpersonal skills and conflict resolution abilities.

How to Answer

Share a specific example, focusing on how you approached the situation and the outcome.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one to understand their perspective and shared my concerns constructively. This open dialogue led to improved collaboration and a more positive team dynamic.”

3. How do you stay updated with the latest trends in machine learning and AI?

This question assesses your commitment to continuous learning.

How to Answer

Discuss the resources you use, such as online courses, conferences, or research papers.

Example

“I stay updated by following leading AI research journals, attending conferences like NeurIPS, and participating in online courses on platforms like Coursera. I also engage with the community through forums and discussions on platforms like GitHub and LinkedIn.”

4. Why do you want to work at Grammarly?

This question gauges your motivation and alignment with the company’s values.

How to Answer

Express your admiration for Grammarly’s mission and how your skills align with their goals.

Example

“I admire Grammarly’s commitment to improving communication through AI. I believe my background in machine learning and passion for creating user-centric solutions align perfectly with your mission to empower users in their writing.”

5. How do you embody the EAGER values in your work?

This question assesses your cultural fit within the company.

How to Answer

Reflect on specific examples that demonstrate your alignment with the company’s values.

Example

“I embody the EAGER values by being adaptable in fast-paced environments, showing empathy towards team members, and striving for excellence in my work. For instance, I actively seek feedback to improve my contributions and support my colleagues in achieving our shared goals.”